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2.
Heliyon ; 10(9): e30616, 2024 May 15.
Article in English | MEDLINE | ID: mdl-38774083

ABSTRACT

Purpose: The objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis. Methods: AML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs. Results: 816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups. Conclusion: LncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

3.
HLA ; 103(4): e15456, 2024 Apr.
Article in English | MEDLINE | ID: mdl-38575336

ABSTRACT

HLA-DPA1*02:117 differs from HLA-DPA1*02:02:02:01 by one nucleotide in exon 2.


Subject(s)
HLA-DP alpha-Chains , Nucleotides , Humans , Alleles , HLA-DP alpha-Chains/genetics , China , Sequence Analysis, DNA
4.
HLA ; 103(3): e15431, 2024 Mar.
Article in English | MEDLINE | ID: mdl-38526340

ABSTRACT

HLA-DPB1*05:01:21 differs from HLA-DPB1*05:01:01:01 by one nucleotide in exon 3.


Subject(s)
HLA-DP beta-Chains , Nucleotides , Humans , Alleles , Base Sequence , China
5.
HLA ; 103(2): e15379, 2024 Feb.
Article in English | MEDLINE | ID: mdl-38342849

ABSTRACT

HLA-C*03:651 differs from HLA-C*03:03:01:01 by one nucleotide in exon 4.


Subject(s)
HLA-C Antigens , Nucleotides , Humans , HLA-C Antigens/genetics , Alleles , Base Sequence , China , Sequence Analysis, DNA
6.
HLA ; 103(2): e15390, 2024 Feb.
Article in English | MEDLINE | ID: mdl-38358086

ABSTRACT

HLA-B*40:555 differs from HLA-B*40:01:02:01 by one nucleotide in exon 3.


Subject(s)
HLA-B Antigens , Nucleotides , Humans , Alleles , Sequence Analysis, DNA , HLA-B Antigens/genetics , China
7.
HLA ; 103(1): e15329, 2024 Jan.
Article in English | MEDLINE | ID: mdl-38174646

ABSTRACT

HLA-C*17:69 differs from HLA-C*17:01:01:02 by one nucleotide in exon 4.


Subject(s)
HLA-C Antigens , Nucleotides , Humans , HLA-C Antigens/genetics , Alleles , Base Sequence , China , Sequence Analysis, DNA
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